Methods and systems for physiologically informed account metrics utilizing artificial intelligence

ABSTRACT

A system for physiologically informed account metrics. The system includes a computing device configured to receive from a remote device operated by a third party, an account inquiry. The computing device is further configured to identify a biological extraction related to a particular user. The computing device is further configured to calculate a user account profile utilizing the biological extraction wherein the user account profile contains at least an element of user behavior data and at least an element of user hazard data. The computing device is further configured to generate an account machine-learning model and determine a response to the account inquiry utilizing an account metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 16/781,540 filed on Feb. 4, 2020 and entitled “METHODS AND SYSTEMSFOR PHYSIOLOGICALLY INFORMED ACCOUNT METRICS UTILIZING ARTIFICIALINTELLIGENCE,” the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for physiologically informed account metricsutilizing artificial intelligence.

BACKGROUND

Account metrics can often be difficult to calculate as there are manyfactors to consider. Furthermore, factors utilized to measure andcalculate account metrics frequently shift and change. Currently, therelacks an ability to calculate account metrics informed by physiologicaldata.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for physiologically informed account metricsutilizing artificial intelligence, the system including a computingdevice the computing device designed and configured to receive, from aremote device operated by a third party, an account inquiry; identify abiological extraction related to the particular user; calculate a useraccount profile utilizing the user biological extraction, wherein theuser account profile contains at least an element of user hazard data,wherein in hazard data describes a user's predisposition to monetaryrisk based on a user's biological extraction; generate an accountmachine-learning model, wherein the account machine-learning modelutilizes the user account profile as an input and outputs an accountmetric; and determine a response to the account inquiry utilizing theaccount metric.

In an aspect, a method of physiologically informed account metricsutilizing artificial intelligence, the method including receiving, by acomputing device, from a remote device operated by a third party, anaccount inquiry; identifying, by the computing device, a biologicalextraction related to the particular user; calculating, by the computingdevice, a user account profile utilizing the user biological extraction,wherein the user account profile contains at least an element of userhazard data, wherein in hazard data describes a user's predisposition tomonetary risk based on a user's biological extraction; generating, bythe computing device, an account machine-learning model, wherein theaccount machine-learning model utilizes the user account profile as aninput and outputs an account metric; and determining, by the computingdevice, a response to the account inquiry utilizing the account metric.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for physiologically informed account metrics;

FIG. 2 is a block diagram illustrating an exemplary embodiment of acredential database;

FIG. 3 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 5 is a process flow diagram illustrating an exemplary embodiment ofa method of physiologically informed account metrics; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for physiologically informed account metrics. In anembodiment, a computing device receives from a remote device operated bya third party, an account inquiry. An account inquiry identifies aparticular user and an account operation related to the particular user.For instance and without limitation, an account inquiry may contain arequest for authorization for a car compensation for the user. Acomputing device identifies a biological extraction related to the userwherein the biological extraction contains at least an element of userphysiological data. A computing device calculates a user account profileutilizing a user biological extraction. A computing device generates anaccount machine-learning model that utilizes a fiscal profile as aninput and outputs an account metric. A computing device determines aresponse to an account inquiry utilizing an account metric.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forphysiologically informed account metric is illustrated. System 100includes a computing device 102. Computing device 102 may include anycomputing device 102 as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device 102 may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. Computingdevice 102 may include a single computing device 102 operatingindependently or may include two or more computing device 102 operatingin concert, in parallel, sequentially or the like; two or more computingdevices 102 may be included together in a single computing device 102 orin two or more computing devices 102. Computing device 102 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 102 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computing devices102, and any combinations thereof. A network may employ a wired and/or awireless mode of communication. In general, any network topology may beused. Information (e.g., data, software etc.) may be communicated toand/or from a computer and/or a computing device 102. Computing device102 may include but is not limited to, for example, a computing device102 or cluster of computing devices 102 in a first location and a secondcomputing device 102 or cluster of computing devices 102 in a secondlocation. Computing device 102 may include one or more computing devices102 dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device 102 may distribute one ormore computing tasks as described below across a plurality of computingdevices 102 of computing device 102, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices 102. Computing device 102 maybe implemented using a “shared nothing” architecture in which data iscached at the worker; in an embodiment, this may enable scalability ofsystem 100 and/or computing device 102.

Still referring to FIG. 1 , computing device 102 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, computing device 102 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device 102 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , computing device 102 is configuredto receive, from a remote device 104 operated by a third party, anaccount inquiry. Remote device 104 may include without limitation, adisplay in communication with computing device 102, where a display mayinclude any display as described herein. Remote device 104 may includean additional computing device, such as a mobile device, laptop,desktop, computer and the like. Remote device 104 may be operated by auser, where a user may include any human being. Computing device 102 mayreceive a transmission from a remote device 104 utilizing any networkmethodology as described herein.

With continued reference to FIG. 1 , computing device 102 is configuredto receive from a remote device 104 operated by a third party, anaccount inquiry 106. An “account inquiry,” as used in this disclosure,is an element of data describing any previous, current and/or futureagreement or communication carried out between a customer and a vendorto acquire an asset for compensation. An account inquiry 106 may includea proposed agreement such as an acquisition where an article may beacquired for income. An account inquiry 106 may include a proposedagreement such as an account where a lender may give a user an amount ofincome to borrow in return for smaller repayments from a borrower to thelender over time with or without interest compensations. An accountinquiry 106 may include a proposed bank agreement where a lender maygive a large amount of income to a borrower for the acquisition of anarticle such as a house. An account inquiry 106 may include one or moreproposed monetary deals from a bank such as an account and/or bankagreement. An account inquiry 106 may include a proposed request for anaccount balance card compensation. An account inquiry 106 may bereceived from a remote device 104 operated by a third party. A “thirdparty,” as used in this disclosure, includes any party other than a userwho is and/or who may become a party to a fiscal arrangement with theuser. A third party may include any individual, public or private group,and/or monetary institution that may make income available to a userthat will be repaid. Repayment may include repayment of the principalamount and may include an interest charge. Repayment may occur inincrements such as monthly or yearly compensations and/or may occur as alump sum. For instance and without limitation, a third party may includea central bank such as for example the Federal Reserve Bank, a bank, aninternet bank, a saving bank, an account balance union, an accountbalance association, a coverage company, an account balance cardcompany, and the like.

With continued reference to FIG. 1 , computing device 102 is configuredto receive from a remote device 104 an account inquiry 106 thatidentifies a particular user and an account operation 108 related to theparticular user. A “particular user,” as used in this disclosure, is theidentification of any human being who is seeking to enter into anymonetary transaction with a third-party. For instance and withoutlimitation, a particular user may identify a 27 year old female who isseeking a loan from her neighborhood branch bank in order to mortgage ahouse. In yet another non-limiting example, a particular user mayidentify a 57 year old male who is seeking to acquire a motorboat on hisaccount credit card issued by an account credit card company. Computingdevice 102 receives an account inquiry 106 that contains an accountoperation 108 related to the particular user. A “account operation,” asused in this disclosure, includes data describing any previous, current,and/or proposed monetary agreement between a particular user and athird-party. For example, an account operation 108 may describe anapplication by a sixteen year old male for a debit card to be issuedfrom his neighborhood bank where he has a checking account. In yetanother non-limiting example, an account operation 108 may describe anapplication by a twenty one year old female to a lender to refinance astudent loan. In yet another non-limiting example, an account operation108 may describe an application by a 67 year old male to obtain anautomobile loan from a lending company.

With continued reference to FIG. 1 , computing device 102 is configuredto authenticate an account inquiry 106 to protect against stolenidentities and potential credit thieves. Computing device 102 isconfigured to transmit to a remote device 104 operated by a third partyan authentication request 110. An “authentication request,” as used inthis disclosure, is any request to prove an assertion, including theidentity of a third-party remote device 104 and/or a particular user. Anauthentication request 110 may include a knowledge factor that mayrequire a correct response to something a third-party may know such as apassword, partial password, pass phrase, security question and/orpersonal identification number. An authentication request 110 mayinclude an ownership factor that may require proof of something athird-party may have such as a wrist band, identification card, securitytoken, implanted device, cell phone with built-in hardware token,software token, and/or remote device 104 containing a software token. Anauthentication request 110 may include an inherence factor that mayrequire proof of a third-party and/or a particular user including forexample a biometric identifier such as a fingerprint, retinal pattern,DNA sequence, signature, face, voice, unique bio-electric signals andthe like. Computing device 102 may transmit a single factorauthentication such as when only one factor is utilized to authenticatea third party. For example, a single factor authentication may occurwhen only a single knowledge factor such as a password is utilized toauthenticate a third party. Computing device 102 may transmit amulti-factor authentication such as when a computing device 102 mayrequire a third-party to authenticate a password and a software token orwhen a computing device 102 may require a third-party to authenticate acryptographic public/private key pair in addition to a personalidentification number (PIN). Computing device 102 receives an identifier112 of a particular user. An “identifier,” as used in this disclosure,is data describing information used to confirm the identity of aparticular user. An identifier 112 may include the full name of theparticular user, the address of the particular user, the phone number ofthe particular user, the social security number of the user, thedriver's license number of the particular user, the passport number ofthe particular user, a universally unique identifier (UUID), a globallyunique identifier (GUID), a universal identification number (UIN) of theparticular user, a global identification number (GIN) of the particularuser and the like. Computing device 102 validates an identifier 112 ofthe particular user. Computing device 102 may validate the identifier112 of the particular user such as by comparing the received identifier112 of the particular user to one or more stored identifier 112contained within system 100. For example, computing device 102 maycompare a received identifier 112 of a particular user that contains theuser's social security number to a stored social security number for theuser to determine the authenticity of the third-party and/or theparticular user.

With continued reference to FIG. 1 , system 100 may include a credentialdatabase 114. Credential database 114 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other form or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Credential database 114may contain one or more identifier 112 of a particular user as describedbelow in more detail.

With continued reference to FIG. 1 , computing device 102 is configuredto identify a biological extraction 116 related to a particular user. A“biological extraction,” as used in this disclosure, contains at leastan element of user physiological data. As used in this disclosure,“physiological data” is any data indicative of a person's physiologicalstate; physiological state may be evaluated with regard to one or moremeasures of health of a person's body, one or more systems within aperson's body such as a circulatory system, a digestive system, anervous system, or the like, one or more organs within a person's body,and/or any other subdivision of a person's body useful for diagnostic orprognostic purposes. For instance, and without limitation, a particularset of biomarkers, test results, and/or biochemical information may berecognized in a given medical field as useful for identifying variousdisease conditions or prognoses within a relevant field. As anon-limiting example, and without limitation, physiological datadescribing red blood cells, such as red blood cell count, hemoglobinlevels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1 , physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1 , physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C(HbAlc)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1 , physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1 , physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1 , physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing module136 as described in this disclosure.

Still referring to FIG. 1 , physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which as used hereinincludes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, asdescribed in further detail below.

With continuing reference to FIG. 1 , physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1 , physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a server 104 may present to usera set of assessment questions designed or intended to evaluate a currentstate of mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; at least a server 104 mayprovide user-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1 , assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1 , physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1 , physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1 , user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1 , epigenetic, as used herein,includes any user body measurements describing changes to a genome thatdo not involve corresponding changes in nucleotide sequence. Epigeneticbody measurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior pattern 126. This may include effects oncellular and physiological phenotypic traits that may occur due toexternal or environmental factors. For example, DNA methylation andhistone modification may alter phenotypic expression of genes withoutaltering underlying DNA sequence. Epigenetic body measurements mayinclude data describing one or more states of methylation of geneticmaterial.

With continued reference to FIG. 1 , gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1 , gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,campylobacter species, Clostridium difficile, cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1 , microbiome, as used herein,includes ecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen based breath tests, fructose basedbreath tests. Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1 , nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1 , nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1 , genetic as used herein, includesany inherited trait. Inherited traits may include genetic materialcontained with DNA including for example, nucleotides. Nucleotidesinclude adenine (A), cytosine (C), guanine (G), and thymine (T). Geneticinformation may be contained within the specific sequence of anindividual's nucleotides and sequence throughout a gene or DNA chain.Genetics may include how a particular genetic sequence may contribute toa tendency to develop a certain disease such as cancer or Alzheimer'sdisease.

With continued reference to FIG. 1 , genetic body measurement mayinclude one or more results from one or more blood tests, hair tests,skin tests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1 , genetic body measurement mayinclude an analysis of COMT gene that is responsible for producingenzymes that metabolize neurotransmitters. Genetic body measurement mayinclude an analysis of DRD2 gene that produces dopamine receptors in thebrain. Genetic body measurement may include an analysis of ADRA2B genethat produces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vaso-dilation and vaso-constriction of blood vessels.

With continued reference to FIG. 1 , genetic body measurement mayinclude ACE gene that is involved in producing enzymes that regulateblood pressure. Genetic body measurement may include SLCO1B1 gene thatdirects pharmaceutical compounds such as statins into cells. Geneticbody measurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fulness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1 , genetic body measurement mayinclude CYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1 , metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1 , physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MRI) equipment, ultrasoundequipment, optical scanning equipment such as photo-plethysmographicequipment, or the like. A sensor may include any electromagnetic sensor,including without limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100.

With continued reference to FIG. 1 , computing device 102 may retrieve abiological extraction 116 related to a particular user from biologicalextraction database 118. Biological extraction database 118 may beimplemented as any data structure suitable for use as credentialdatabase 114. Biological extraction database 118 may contain one or morebiological extraction 116 pertaining to a user as described below inmore detail.

With continued reference to FIG. 1 , computing device 102 is configuredto calculate a user account profile 120 utilizing a user biologicalextraction 116. A “user account profile,” as used in this disclosure, isa compilation of data containing elements utilized by computing device102 to calculate an account metric. Elements may include one or moreindicators of a user's monetary patterns, risky behavior, demographics,household, other monetary obligations and the like. A user accountprofile 120 contains an element of user behavior data 122. An “elementof user behavior data,” as used in this disclosure, is data describingany concurrent and/or previous user behavior relating to any previous,current, and/or future account operation 108. For instance and withoutlimitation, an element of user behavior data 122 may contain informationdescribing a series of installment payments on a mortgage loan that auser missed paying. In yet another non-limiting example, an element ofuser behavior data 122 may contain information describing a history ofcredit card payments that were late or never paid.

With continued reference to FIG. 1 , an element of user behavior data122 may be identified by a user entry; for instance, and withoutlimitation, a computing device 102 may provide a user with aquestionnaire in the form of one or more data fields requesting that theuser identify any previous, current, and/or future account operation108. Questions presented to a user may ask for information from otherprevious third-parties that a user may have tried to seek funding and/ora loan from. Questions presented to a user may ask for names andaddresses of other third-parties such as the name and address of athird-party that a user utilized to fund a car loan. In yet anothernon-limiting example, questions presented to a user may ask for previousbank statements, tax documents, checking account balance information,saving account information and the like. Questions presented to a usermay ask one or more questions regarding how risk seeking and/or how riskaverse a user acts in regard to monetary transactions.

With continued reference to FIG. 1 , computing device 102 identifies anelement of user behavior data 122 utilizing an account classifier 124.Computing device 102 is configured to receive a plurality of dataentries containing at least an element of data pertaining to a previoususer account operation 108. An “element of data pertaining to a previoususer account operation,” as used in this disclosure, is data describingany aspect of any previous account operation 108 that a user wasinvolved in. An element of data pertaining to a previous user accountoperation 108 may describe a loan amount that a user applied for and wasdenied from. An element of data pertaining to a previous user accountoperation 108 may describe the timeliness of a series of payments madein response to a loan for wedding expenses. An element of datapertaining to a previous user account operation 108 may describe theamount of money that a user took out a second mortgage on a property.

With continued reference to FIG. 1 , computing device 102 classifiesusing an account classifier 124, a plurality of data entries to abehavior pattern. A “classifier,” as used in this disclosure, is amachine-learning model, such as a mathematical model, neural net, orprogram generated by a machine-learning algorithm known as aclassification algorithm, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A “account classifier,” as used in this disclosure, is aclassifier configured to input at least a previous user account metricand outputs a behavior pattern 126. Account classifier 124 may begenerated using a classification algorithm, defined as a process wherebya computing device 102 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. Trainingdata includes any of the training data as described below in moredetail. A “behavior pattern,” as used in this disclosure, is datadescribing based on a level of analysis of a user's previous accountoperation 108, the creditworthiness of a particular user. A behaviorpattern 126 may indicate the creditworthiness of a particular user byreflecting how likely the particular user is to repay debts. A behaviorpattern 126 may indicate how likely a particular user is to pay back aloan based on a user's previous account operation 108. A behaviorpattern 126 may indicate creditworthiness based on monetary amountsand/or quantities of previous account operation 108. A behavior pattern126 may indicate creditworthiness based on previously calculated creditscores. Computing device 102 identifies an element of user behavior data122 utilizing an output behavior pattern 126. In an embodiment, anelement of user behavior data 122 may contain any information containedwithin a behavior pattern 126.

With continued reference to FIG. 1 , computing device 102 is configuredto calculate a user account profile 120 to contain at least an elementof user hazard data 128. “User hazard data,” as used in this disclosure,is data describing a user's predisposition to monetary risk 130 based ona user's biological extraction 116. A “monetary risk,” as used in thisdisclosure, is any type of risk associated with financing, including anymonetary transaction that puts a user in risk of default. Monetary risk130 may include market risk, liquidity risk, concentration risk, creditrisk, reinvestment risk, inflation risk, horizon risk, longevity risk,income risks, expense risks, investment and asset risks, debit and/orcredit card risks. For example, monetary risk 130 may include an incomerisk that may be caused by death, disability, unemployment, and/oraging. Monetary risk 130 may include an expense risk that may be causedby high expense, and/or an emergency expense. Monetary risk 130 mayinclude an investment and/or asset risk that may be caused by riskyinvestments, inflation, depreciation, destruction, and/or theft.Monetary risk 130 may include a debit and/or credit card risk caused bytoo much debt, bad debt, and/or bad credit scores.

With continued reference to FIG. 1 , computing device 102 is configuredto retrieve a second biological extraction 116 related to a particularuser. Computing device 102 may retrieve a second biological extraction116 from biological extraction database 118. Computing device 102 isconfigured to receive hazard training data 132. “Hazard training data,”as used in this disclosure, is training data that contains a pluralityof biological extraction 116 and a plurality of hazard labels. “Trainingdata,” as used in this disclosure, is data containing correlations thata machine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1 , trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by computing device 102 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

With continued reference to FIG. 1 , a “hazard label,” as used in thisdisclosure, is data describing an indication as to a user'spredisposition to monetary risk 130 based on a user's biologicalextraction 116. In an embodiment, a user's predisposition to monetaryrisk 130 may be graded on a continuum. For example, a user who shows nopredisposition to monetary risk 130 may be given a hazard label 134 thatindicates very low predisposition to monetary risk 130 while a user whoshows some predisposition to monetary risk 130 may be given a hazardlabel 134 that indicates moderate predisposition to monetary risk 130,and a user who shows a great deal of predisposition to monetary risk 130may be given a hazard label 134 that indicates high predisposition tomonetary risk 130.

With continued reference to FIG. 1 , computing device 102 is configuredto utilize hazard training data 132 to generate a hazardmachine-learning model 136. A “hazard machine-learning model,” as usedin this disclosure, is a machine-learning model that utilizes a secondbiological extraction 116 related to a particular user as an input andoutputs a hazard label 134. A machine-learning model, as used herein, isa mathematical representation of a relationship between inputs andoutputs, as generated using any machine-learning process includingwithout limitation any process as described above, and stored in memory;an input is submitted to a machine-learning model once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model may be generated by creating an artificial neuralnetwork, such as a convolutional neural network comprising an inputlayer of nodes, one or more intermediate layers, and an output layer ofnodes. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

With continued reference to FIG. 1 , a machine-learning process, alsoreferred to as a machine-learning algorithm, is a process thatautomatedly uses training data and/or a training set as described aboveto generate an algorithm that will be performed by a computing device102 and/or module to produce outputs given data provided as inputs; thisis in contrast to a non-machine-learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Continuing to refer to FIG. 1 , machine-learning algorithms may beimplemented using techniques for development of linear regressionmodels. Linear regression models may include ordinary least squaresregression, which aims to minimize the square of the difference betweenpredicted outcomes and actual outcomes according to an appropriate normfor measuring such a difference (e.g. a vector-space distance norm);coefficients of the resulting linear equation may be modified to improveminimization. Linear regression models may include ridge regressionmethods, where the function to be minimized includes the least-squaresfunction plus term multiplying the square of each coefficient by ascalar amount to penalize large coefficients. Linear regression modelsmay include least absolute shrinkage and selection operator (LASSO)models, in which ridge regression is combined with multiplying theleast-squares term by a factor of 1 divided by double the number ofsamples. Linear regression models may include a multi-task lasso modelwherein the norm applied in the least-squares term of the lasso model isthe Frobenius norm amounting to the square root of the sum of squares ofall terms. Linear regression models may include the elastic net model, amulti-task elastic net model, a least angle regression model, a LARSlasso model, an orthogonal matching pursuit model, a Bayesian regressionmodel, a logistic regression model, a stochastic gradient descent model,a perceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure,

Still referring to FIG. 1 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

With continued reference to FIG. 1 , models may be generated usingalternative or additional artificial intelligence methods, includingwithout limitation by creating an artificial neural network, such as aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning. Thisnetwork may be trained using training data.

Still referring to FIG. 1 , machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine-learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised machine-learning processmay include a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a given inputand/or combination of elements inputs is associated with a given outputto minimize the probability that a given input is not associated with agiven output. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in training data. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various possible variations of supervised machine-learningalgorithms that may be used to determine relation between inputs andoutputs.

With continued reference to FIG. 1 , supervised machine-learningprocesses may include classification algorithms, defined as processeswhereby a computing device 102 derives, from training data, a model forsorting inputs into categories or bins of data. Classification may beperformed using, without limitation, linear classifiers such as withoutlimitation logistic regression and/or naive Bayes classifiers, nearestneighbor classifiers including without limitation k-nearest neighborsclassifiers, support vector machines, decision trees, boosted trees,random forest classifiers, and/or neural network-based classifiers.

Still referring to FIG. 1 , machine-learning processes may includeunsupervised processes. An unsupervised machine-learning process, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. Unsupervised processes may not require a responsevariable; unsupervised processes may be used to find interestingpatterns and/or inferences between variables, to determine a degree ofcorrelation between two or more variables, or the like. Unsupervisedmachine-learning algorithms may include, without limitation, clusteringalgorithms and/or cluster analysis processes, such as without limitationhierarchical clustering, centroid clustering, distribution clustering,clustering using density models, subspace models, group models,graph-based models, signed graph models, neural models, or the like.Unsupervised learning may be performed by neural networks and/or deeplearning protocols as described above.

With continued reference to FIG. 1 , computing device 102 identifies anelement of user hazard data 128 that may be contained within fiscalprofile utilizing a hazard label 134. In an embodiment, an element ofuser hazard data 128 may include a hazard label 134. In yet anothernon-limiting example, an element of user hazard data 128 may include asecond biological extraction 116 utilized to generate hazardmachine-learning model 136.

With continued reference to FIG. 1 , computing device 102 is configuredto calculate a user account profile 120 to contain an element of userpersonal profile information 138. “User personal profile information,”as used in this disclosure, is any data that describes any informationrelating to an identifiable person. An element of personal profileinformation may include demographic information such as the age, gender,income, marital status, home address, allergies, educational background,medical diagnoses, medical history, and the like. An element of personalprofile information may include data that describes well thought-outdecisions, and consistent behavior of a user. Consistent behavior mayinclude indicators that show continued employment, stable income,carrying little debt, maintaining a home, having continued access totransportation, residing and/or working in safe neighborhoods and areas,access to parks and playgrounds, walkability of where someone lives.Consistent behavior may include one or more markers of a user'seducation including literacy, languages spoken, early childhoodeducation, vocational training, and higher education. Consistentbehavior may include one or more markers of nutrition that reflectlittle experienced hunger, ability to consume fresh produce, and accessto healthy options. An element of user personal profile information 138may include any data describing any behavior that indicates integrationand/or engagement with a local community. Local communities may includeany social unit that shares one or more common norms, religion, values,customs and/or identity. Local communities may share a sense of placesituated in a given geographical area and/or share a virtual spacethrough communication platforms. Local communities may be based onlocation such as communities of place that range from localneighborhoods, suburbs, villages, towns, cities, regions, nations,and/or planet as a whole. Local communities may be identity basedcommunities including local cliques, sub-cultures, ethnic groups,religions, multicultural and/or pluralistic civilizations and the like.Local communities may be based on family and/or network based guildssuch as incorporated associations, political decision making structures,economic enterprises, and/or professional associations. An element ofuser personal profile information 138 may indicate any socialintegration, support systems, and/or community engagement that a userhas participated in. For instance and without limitation, an element ofuser personal profile information 138 may indicate that a user engagesin a weekly choir group or volunteers once per month at a soup kitchen.Computing device 102 may calculate a user account profile 120 to containat least an element of user personal profile information 138. An elementof user personal profile information 138 contained within a user accountprofile 120 may be utilized to generate an account machine-learningmodel as described below in more detail. One or more elements of userpersonal profile information may be stored within user database 140.User database 140 may be implemented as any data structure suitable foruse as credential database 114 as described below in more detail.

With continued reference to FIG. 1 , computing device 102 is configuredto calculate a user account profile 120 to contain a user account score142. A “user account score,” as used in this disclosure, is any textual,numerical, and/or symbolic data representing one or more indicators ofcreditworthiness. Creditworthiness may reflect how likely a user is todefault on any debts and/or how worth a user may be to receive newcredit. Creditworthiness may reflect how likely a user is to repay adebt. Creditworthiness may be utilized to approximate how suitable auser is for a loan or how likely it is that you will be able to makepayments on a loan. A user account score 142 may include an accounthistory factor, an outstanding account factor, an account length factor,and/or an account type factor. A “account history factor,” as used inthis disclosure, is any data that contains information pertaining to auser's previous fiscal inquires and/or previous account operation 108.An account history factor may contain information that describes if auser has made payments on time, how often a user missed payments, howmany days past due a user paid bills, and how recently a payment mayhave been missed. For example, an account history factor may indicatethat a user paid three credit card bills on time and in full over thecourse of the past three months. In yet another non-limiting example, anaccount history factor may indicate that a user missed payment on oneinstallment payment on a user's mortgage but has paid every otherpayment on time. An “outstanding account factor,” as used in thisdisclosure, is any data that contains information describing anyoutstanding monetary balances. An outstanding monetary balance mayinclude any credit limit that has been utilized. For example, anoutstanding monetary balance may include a credit card bill that has notbeen paid off in full or a monetary loan that has a balance still owed.An outstanding account factor may include a breakdown of creditutilization that may reflect the amount of a user's credit limit thathas been utilized. An outstanding account factor may be based on anentire monetary amount that a user owes, the number and types ofaccounts that a user has available, and/or the proportion of money owedcompared to how much credit is available. A “account length factor,” asused in this disclosure, is any data that contains informationdescribing the length of any current or previous account operation 108,and/or the length of any current or previous fiscal inquiries. Forexample, an account length factor may indicate that a user has a historyof making timely payments. In yet another non-limiting example, anaccount length factor may indicate that a user has a very brief andshort history of making timely payments. A “account type factor,” asused in this disclosure, is data describing any type of fiscal inquiriesthat a user is currently engaged with. An account type factor maydescribe any open accounts, installment loans, home loans, retail creditcards, credit cards and the like. An account type factor may describethat a user has a home loan and a credit card loan. In yet anothernon-limiting example, an account type factor may describe that a userhas two separate mortgage installment loans. In an embodiment, a useraccount score 142 may be calculated based on combining one or morefactors. In an embodiment, user account score 142 may be calculated bymultiplying an account history factor, an outstanding account factor, anaccount length factor, and an account type factor together to produceone final sum. In an embodiment, a user account score 142 may becalculated by adding an account history factor, an outstanding accountfactor, an account length factor, and an account type factor together toproduce a user account score 142. In an embodiment, one or more factorsmay be subtracted, and/or divided together based on one or more expertinputs regarding the best way to calculate a user account score.

With continued reference to FIG. 1 , computing device 102 is configuredto generate an account machine-learning model 144. A “accountmachine-learning model,” as used in this disclosure, is amachine-learning model that utilizes a fiscal profile as an input andoutputs an account metric 146. Account machine-learning model 144includes any of the machine-learning models as described above. An“account metric,” as used in this disclosure, is any textual, pictorial,and/or character data that reflects the monetary well-being and/ormonetary stability of a particular user. An account metric 146 maycontain an indication as to the credit worthiness of a user such as howmuch risk a lender may take when a user borrows money. An account metric146 may contain a numerical number indicating a certain dollar amountand/or dollar limit that a user is approved for. An account metric 146may contain a determination of a user's credit risk. An account metric146 may reflect a likelihood of a user to repay debt responsibly basedon a user's past credit history and current credit status. For instanceand without limitation, an account metric 146 may indicate that a userearns a stable income and has ample money in his bank account to cover aloan of up to $500,000. In yet another non-limiting example, an accountmetric 146 may describe a user as not being particularly monetarilyworthy because the user does not have a stable job, has seasonalemployment and does not have a history of making payments on time.Computing device 102 generates account machine-learning model 144utilizing account training data 148. “Account training data,” as used inthis disclosure, is training data that contains a plurality of fiscalprofiles and a plurality of correlated account metric 146. Training dataincludes any of the training data as described above. Computing device102 is configured to receive account training data 148. Computing device102 generates account machine-learning model 144 utilizing accounttraining data 148 and a first machine-learning algorithm.

With continued reference to FIG. 1 , computing device 102 is configuredto determine a response 150 to an account inquiry 106 utilizing anaccount metric 146. A “response,” as used in this disclosure, is anytextual, numerical, and/or character data generated in reply to anaccount inquiry 106. A response 150 may contain an answer to a questioncontained within an account inquiry 106. A response 150 may contain anapproval of a requested purchase, loan, mortgage, line of credit and thelike contained within an account inquiry 106. A response 150 may containa denial of a requested purchase, loan, mortgage, line of credit and thelike contained within an account inquiry 106. A response 150 may containa modified loan amount, mortgage amount, line of credit, and the like. Amodification may include an increased approval amount for a requestcontained within an account inquiry 106 and/or a decreased approvalamount for a request contained within an account inquiry 106. Forexample, a response 150 may contain a denial of a requested purchase of$50,000 because an account metric 146 indicates that a user is a riskyinvestment seeker and has a genetic mutation to monoamine oxidase A genemaking the user more likely to carry large amounts of debt.

With continued reference to FIG. 1 , computing device 102 is configuredto determine if account inquiry 106 is in excess of an account metric146 and deny the account inquiry 106. For example, computing device 102may determine that an account inquiry 106 for a $100,000 loan is inexcess of an account metric 146 that indicates the user has a network of$20,000 and has a genetic tendency to be a risk taker. Computing device102 is configured to determine that an account inquiry 106 does notexceed an account metric 146 and approve an account inquiry 106. Forinstance and without limitation, computing device 102 may determine thatan account inquiry 106 to make a $5000 credit card purchase does notexceed an account metric 146 that determines that a user has a stableincome, does not have a predisposition to impulsivity, and only utilizes5% of his total credit limit on average each month.

With continued reference to FIG. 1 , computing device 102 may determinea response to an account inquiry 106 utilizing a calculated usereffective age 152. A “user effective age 152,” as used in thisdisclosure, is an age of a user as adjusted to reflect a life expectancythat differs from an actuarially projected life expectancy. Forinstance, a user effective age 152 of a person predicted to live feweryears than actuarially projected may be higher than a user effective age152 of a person predicted to match and/or exceed an actuariallyprojected life expectancy. User effective age 152 may be used as arepresentation of a user's likely overall state of health, inasmuch as auser's likelihood to exceed or fall short of actuarially projected lifeexpectancy may be closely linked to a user's state of health. A user's“chronological age,” as defined in this disclosure, is an age of theuser as measured in years, or other units of time, from the date of theuser's birth to the date of the measurement, where a “date” may includeany calendar date, Julian date, or the like. A chronological age may beused to project a user's “actuarial life expectancy,” defined as aprobable age of death, as predicted using any actuarial method and/ortable, and/or an interval from a date such as the present date to theprobable age of death; actuarial methods may include looking up and/orcalculating a user's life expectancy using date of birth and/ordemographic information about the user such as sex, ethnicity,geographic location, nationality, or the like. A user effective age 152may be calculated based on a user's chronological age and a user'sbiological extraction 116. For instance and without limitation,computing device 102 may add several years to a user's chronological ageto output an effective age that is older than a user's chronological agewhen a user's biological extraction 116 contains abnormal findings or alaboratory finding that is outside of normal limits. In yet anothernon-limiting example, computing device 102 may subtract several years toa user's chronological age to output an effective age that is youngerthan a user's chronological age when a user's biological extraction 116contains normal findings or a laboratory finding that is within and/orbelow normally accepted limits.

With continued reference to FIG. 1 , user effective age 152 may becalculated by multiplying a telomer length factor by an endocrinalfactor multiplied by a histone variance factor to produce a positiveeffective age score. A “telomer length factor,” as used in thisdisclosure, is a factor that may be multiplied by a user's chronologicalage to reflect an effect that telomeric length and/or a change intelomere length has on the user's effective age. Calculation may includeprediction of a variance from actuarial life expectancy for a givenperson, as defined above, as determined based on telomeric length and/orvariation in telomere length. A difference between these two values maybe added to a user chronological age and then divided by the userchronological age to calculate a “raw” factor, for instance as describedabove; this may then be multiplied by a weight to determine the telomerlength factor, whereas above the weight may be calculated to offsetrelatedness between telomere length and/or change in telomere length andother elements used to calculate age factors as described herein, suchas endocrinal age factors. A computing device 102 may determine telomerlength factor by retrieving telomer length factor from user database140. User database 140 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other form or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. For instance, and without limitation, oneor more experts may enter data in user database 140 indicative of aneffect on user life expectancy; such data may, for instance, be enteredas described in further detail below.

With continued reference to FIG. 1 , an “endocrinal factor,” as used inthis disclosure, is a factor that may be multiplied by a user'schronological age to reflect an effect that endocrinal data has on theuser's effective age. Endocrinal data may include any physiological datarelating to the endocrine system. The endocrine system includes glandsthat include the pineal gland, the thyroid gland, the parathyroid gland,the pituitary gland, the adrenal gland, the pancreas, the ovaries, andthe testis. Endocrinal data may include one or more measurements offunction of the endocrine system such as for example, a measurement ofthyroid stimulating hormone (TSH) or a fasting serum insulin level.Calculation of an endocrinal factor may include any calculation fortelomer length factor as described above.

With continued reference to FIG. 1 , a “histone variance factor,” asused in this disclosure, is a factor that may be multiplied by a user'schronological age to reflect an effect that loss of histones has on theuser's effective age. Histones include alkaline proteins found in cellnuclei that package and order DNA into nucleosomes. Histones are theprimary component of chromatin, maintaining a role in gene regulation.Histone loss may be linked with cell division, as reduced synthesis ofnew histones has been seen to be corelated with shortened telomeres thatactivate a DNA damage response. Loss of core histones include H2A, H2B,H3, and H4 may be considered an epigenetic mark of aging. Calculation ofa histone variance factor may include any calculation for telomer lengthfactor as described above.

With continued reference to FIG. 1 , computing device 102 may utilize auser effective age 152 in combination with an account metric 146 todetermine a response to an account inquiry 106. For example, computingdevice 102 may utilize a user effective age 152 to determine a loanrequest amount, and/or the length of repayment of a particular monetaryinquiry. For instance and without limitation, a user effective age 152that indicates a user who has a chronological age of 75 and has a usereffective age 152 of 55 may be a suitable candidate for a twenty yearmortgage loan, and as such computing device 102 may generate a responsethat indicates the user is approved for a twenty year loan incombination with a user's account metric 146 that indicates that theuser is monetarily secure. In yet another non-limiting example, a usereffective age 152 that indicates a user who has a chronological age of57 and has a user effective age of 77 may not be a suitable candidatefor a credit high credit limit because of the user's advanced effectiveage and the unlikeliness of the user to be able to pay back a high loanamount.

Referring now to FIG. 2 , an exemplary embodiment 200 of credentialdatabase 114 is illustrated. Credential database 114 may be implementedas any data structure as described above in more detail in reference toFIG. 1 . One or more tables contained within credential database 114 mayinclude biometric identifier table 204; biometric identifier table 204may include one or more biometric identifier 112 pertaining to a userand/or a third party. For instance and without limitation, biometricidentifier table 204 may include a user's fingerprint scan or an irisscan identifying a third-party who works at a monetary establishmentsuch as a bank. One or more tables contained within credential database114 may include transactional monetary account table 208; transactionalmonetary account table 208 may include authentication informationpertaining to a user's bank account number, credit and/or debit accountnumber, and/or personal identification number (PIN).

For instance and without limitation, transactional monetary accounttable 208 may include a bank account number for a user's checkingaccount. One or more tables contained within credential database 114 mayinclude electronic identification credentials table 212; electronicidentification credentials table 212 may include authenticationinformation relating to digital certificates. For instance and withoutlimitation, electronic identification credentials table 212 may includea public and private key pair utilized to authenticate and uniquelyidentify a user and/or a third party. One or more tables containedwithin credential database 114 may include universal identifier table216; universal identifier table 216 may include one or more universalidentifiers that identify a user and/or a third party. For instance andwithout limitation, universal identifier table 216 may include auniversally unique identifier 112 (UUID) and/or a globally uniqueidentifier 112 (GUID) that uniquely identifies a user and/or athird-party. One or more tables contained within credential database 114may include demographic table 220; demographic table 220 may includedemographic information pertaining to a user and/or a third party suchas a user's full legal name, and month, day, and year of birth. Forinstance and without limitation, demographic table 220 may include auser's full legal name, address, and social security number. One or moretables contained within credential database 114 may include passwordtable 224; password table 224 may include one or more passwords used toauthenticate the identity of a user and/or a third party. For instanceand without limitation, password table 224 may include a string ofcharacters that that uniquely identify a third party such as a mortgagelender.

Referring now to FIG. 3 , an exemplary embodiment 300 of biologicalextraction database 118 is illustrated. Biological extraction database118 may be implemented as a data structure as described above inreference to FIG. 1 . Biological extraction database 118 may include oneor more elements of physiological data pertaining to a particular user.Physiological data contained within biological extraction database 118may be organized according to type of biological extraction 116 utilizedto analyze a particular element of physiological data, body system orbody dimension that a particular element of physiological data pertainsto, sample type, category of physiological data and the like. One ormore tables contained within biological extraction database 118 mayinclude microbiome sample table 304; microbiome sample table 304 maycontain one or more elements of physiological data containing amicrobiome sample. For instance and without limitation, microbiomesample table 304 may contain an element of physiological data such as astool sample analyzed for levels of pathogenic bacteria. One or moretables contained within biological extraction database 118 may includefluid sample table 308; fluid sample table 308 may contain one or moreelements of physiological data containing a fluid sample. For instanceand without limitation, fluid sample table 308 may include a salivasample analyzed for one or more hormone levels. One or more tablescontained within biological extraction database 118 may includeintracellular nutrient data table 312; intracellular nutrient data table312 may include one or more elements of physiological data containing anintracellular nutrient level. For instance and without limitation,intracellular nutrient data table 312 may include an intracellular levelof Vitamin C. One or more tables contained within biological extractiondatabase 118 may include microchip sample table 316; microchip sampletable 316 may include one or more elements of physiological dataobtained from a microchip. For instance and without limitation,microchip sample table 316 may include one or more extracellularnutrient levels of coenzyme Q 10 obtained from a microchip embeddedunder the skin. One or more tables contained within biologicalextraction database 118 may include stool sample table 320; stool sampletable 320 may include one or more elements of physiological dataobtained from a stool sample. For instance and without limitation, stoolsample table 320 may include a measurement of a stool pH level. One ormore tables contained within biological extraction database 118 mayinclude tissue sample table 324; tissue sample table 324 may include oneor more elements of physiological data obtained from a tissue sample.For instance and without limitation, tissue sample table 324 may includean intestinal biopsy analyzed for the presence or absence of Celiacdisease.

Referring now to FIG. 4 , an exemplary embodiment 400 of user database140 is illustrated. User database 140 may be implemented as any datastructure suitable for use as credential database 114 as described abovein more detail in reference to FIG. 1 . One or more tables containedwithin user database 140 may include demographic table 404; demographictable 404 may include one or more elements of data pertaining to userdemographic information. For instance and without limitation,demographic table 404 may include information describing a user's name,address, date of birth, marital status, education, income level, hobbiesand the like. One or more tables contained within user database 140 mayinclude behavior table 408; behavior table 408 may include one or moreelements of data pertaining to any concurrent and/or previous userbehavior relating to any account operation 108. For instance and withoutlimitation, behavior table 408 may include any entry that describes auser's consistent payment on an automobile loan over the past fouryears. One or more tables contained within user database 140 may includecommunity table 412; community table 412 may include data describingbehavior that indicates a user's integration and engagement with localcommunity. For instance and without limitation, community table 412 mayinclude data that describes a user's social integration and supportsystems. One or more tables contained within user database 140 mayinclude telomer length factor table 416; telomer length factor table mayinclude one or more data entries containing a user's telomer length. Oneor more tables contained within user database 140 may include endocrinalfactor table 420; endocrinal factor table 420 may include one or moredata entries containing one or more endocrinal factors relating to auser. One or more tables contained within user database 140 may includehistone variance factor table 424; histone variance factor table 424 mayinclude one or more data entries containing one or more histone variancefactors relating to a user.

Referring now to FIG. 5 , an exemplary embodiment 500 of a method ofphysiologically informed account metric 146 is illustrated. At step 505,a computing device receives from a remote device 104 operated by a thirdparty an account inquiry 106. An account inquiry 106 includes datadescribing any current and/or future agreement or communication carriedout between a buyer and a seller to trade an asset for payment, asdescribed above in more detail in reference to FIG. 1 . An accountinquiry 106 may contain a question asking how much money a user isapproved to finance on a car loan. In yet another non-limiting example,an account inquiry 106 may contain an inquiry as to how much money auser is approved to mortgage on a house. An account inquiry 106 maycontain a request for a particular type of loan and dollar amount suchas a title loan, a pawn shop loan, a payday loan, a home equity loan, acredit card cash advance loan and the like. An account inquiry 106 isreceived from a remote device 104 operated by a third party. A remotedevice 104 includes any of the remote device 104 as described above inreference to FIG. 1 . A third party includes any party other than a userwho is and/or who may become involved in a fiscal transaction with theuser as described above in more detail in reference to FIG. 1 . A thirdparty may include a mortgage lender, or a monetary institution such as abank. A third party may include a mortgage banker, a direct lender suchas a bank or credit union, and/or a secondary lender. A third party mayinclude an employee authorized to act on behalf of a corporation and/orlimited liability company such as a bank, credit union, retail store,brick and mortar store, electronic store and the like.

With continued reference to FIG. 5 , account inquiry 106 identifies aparticular user and an account operation 108 related to the particularuser. A particular user, identifies any human being who is seeking toenter into any monetary transaction with a third-party. For example, anaccount inquiry 106 may identify a particular user who is seeking toobtain financing for a car loan from a credit union. In yet anothernon-limiting example, an account inquiry 106 may identify a particularuser who is seeking to open a new credit card with a bank. In yetanother non-limiting example, an account inquiry 106 may identify aparticular user who is seeking to make a purchase at a retail store witha credit card issued by a major credit card company. An account inquiry106 identifies an account operation 108. An account operation 108describes any previous, current, and/or proposed monetary agreementbetween a particular user and a third-party. For example, an accountoperation 108 may identify a particular user as having a checking andsaving account with a specific bank. In yet another non-limitingexample, an account operation 108 may identify a particular user ashaving applied for a line of credit from a particular credit union. Inyet another non-limiting example, an account operation 108 may identifya particular user has currently having a mortgage with a particularmortgage lender.

With continued reference to FIG. 5 , computing device 102 authenticatesan account inquiry 106. Computing device 102 may authenticate an accountinquiry 106 to confirm the identify of a particular user and/or a thirdparty contained within an account inquiry 106. Computing device 102authenticates an account inquiry 106 by transmitting to a remote device104 operated by a third party, an authentication request 110. Computingdevice 102 may transmit an authentication request 110 utilizing anynetwork methodology as described herein. An authentication request 110is any request to prove an assertion, including the identity of athird-party remote device 104 and/or a particular user as describedabove in more detail in reference to FIG. 1 . An authentication request110 may contain a request for a knowledge factor such as a securitytoken. An authentication request 110 may contain a request for aninherence factor that may contain a request for a biometric identifier112 of a user and/or a third party such as a fingerprint scan, irisscan, palm scan, and the like. Computing device 102 receives from aremote device 104 an identifier 112 of a particular user. An identifier112 includes any of the identifier 112 as described above in referenceto FIG. 1 . An identifier 112 may be utilized by computing device 102 toauthenticate the identity of a particular user and/or a third party. Anidentifier 112 may include a universal identification number (UIN) of auser or a global identification number (GIN) of a user. An identifier112 may include a user's social security number. Computing device 102validates an identifier 112 of a particular user. Computing device 102may validate an identifier 112 of a particular user by comparing areceived identifier 112 to one or more identifier 112 stored withincredential database 114. For example, computing device 102 may compare abiometric identifier 112 such as a user's iris scan to a stored irisscan of the user contained within credential database 114. In yetanother non-limiting example, computing device 102 may compare atransactional monetary account number such as a user's checking accountnumber to a stored checking account number contained within credentialdatabase 114. In yet another non-limiting example, computing device 102may authenticate a public and private key pair identifying a third partysuch as a bank to a stored public and private key pair related to thethird party contained within credential database 114.

With continued reference to FIG. 5 , computing device 102 identifies abiological extraction 116 related to a particular user. Computing device102 may store one or more biological extraction 116 within biologicalextraction database 118. A biological extraction 116 includes any of thebiological extraction 116 as described above in reference to FIG. 1 . Abiological extraction 116 contains at least an element of userphysiological data as described above in reference to FIG. 1 . Computingdevice 102 may identify a biological extraction 116 related to aparticular user utilizing any of the authentication measurements asdescribed above. For example, computing device 102 may authenticate anidentifier 112 of a particular user stored within credential database114 to an identifier 112 contained within biological extraction database118.

With continued reference to FIG. 5 , computing device 102 is configuredto calculate a user account profile 120 utilizing a user biologicalextraction 116. A user account profile 120 includes any of the useraccount profile 120 as described above in reference to FIG. 1 . A useraccount profile 120 includes at least an element of user behavior data122 and at least an element of user hazard data 128. An element of userbehavior data 122 includes describes any concurrent and/or previous userbehavior relating to any previous, current, and/or future accountoperation 108. For instance and without limitation, an element of userbehavior data 122 may describe a user's previous on time monthlypayments for an automobile loan. In yet another non-limiting example, anelement of user behavior data 122 may describe a user's current missedpayments on mortgage installment. Computing device 102 may identify anelement of user behavior data 122 utilizing account classifier 124.Computing device 102 receives a plurality of data entries containing atleast an element of data pertaining to a previous user account operation108. For instance and without limitation, data entries may describe anyprevious loans, mortgages, credit card charges, liens, and the like thatthe user was a party to. Computing device 102 classifies a plurality ofdata entries to a behavior pattern 126. Account classifier includes anyof the account classifier 124 as described above in more detail inreference to FIG. 1 . Account classifier 124 utilizes an element of useraccount operation 108 as an input and outputs a behavior pattern 126.Account classifier 124 may generate a classification algorithm.Classification algorithm includes any of the classification algorithmsas described above in more detail in reference to FIG. 1 . A behaviorpattern 126 indicates the creditworthiness of a particular user byreflecting how likely the particular user is to repay debts. Accountclassifier 124 may output a behavior pattern 126 that contains a labelindicating the creditworthiness of a particular user generated byclassification algorithm. Computing device 102 identifies an element ofuser behavior data 122 utilizing output behavior pattern 126. In anembodiment, computing device 102 may identify an element of userbehavior data 122 where the element of user behavior data 122 mayinclude the output behavior pattern 126. In yet another non-limitingexample, computing device 102 may identify an element of user behaviordata 122 where the element of user behavior data 122 may include anindication as to the creditworthiness of a particular user such as alabel indicating the creditworthiness.

With continued reference to FIG. 5 , computing device 102 is configuredto calculate a user account profile 120 that contains at least anelement of user hazard data 128. An element of user hazard data 128 isdata describing a user's predisposition to monetary risk 130 based on auser's biological extraction 116 as described above in more detail inreference to FIG. 1 . Computing device 102 identifies an element of userhazard data 128 utilizing a hazard machine-learning model 136. Computingdevice 102 retrieves a second biological extraction 116 related to aparticular user. Second biological extraction may be stored withinbiological extraction database 118. Computing device 102 receives hazardtraining data 132. Hazard training data 132 includes any of the hazardtraining data 132 as described above in more detail in reference to FIG.1 . Hazard training data 132 includes a plurality of biologicalextraction 116 and a plurality of hazard label 134. Hazard label 134indicate a user's predisposition to monetary risk 130 based on a user'sbiological extraction 116. A hazard label 134 may describe a user'spredisposition to monetary risk 130 based on a continuum. For instanceand without limitation, a biological extraction 116 showing a mutationto 7R dopamine receptor D4 gene (DRD4), may cause computing device 102to generate a hazard label 134 that indicates a user has a highpredisposition to monetary risk 130 while a user who does not have amutation to DRD4 gene may indicate that a user does not have a highpredisposition to monetary risk 130. Computing device 102 generateshazard machine-learning model 136 utilizing any of the machine-learningalgorithms as described above more detail in reference to FIG. 1 .Hazard machine-learning model 136 utilizes a second biologicalextraction 116 related to a particular user as an input and outputs ahazard label 134. For example, computing device 102 may generate hazardmachine-learning model 136 as a supervised machine-learning model. Inyet another non-limiting example, computing device 102 may generatehazard machine-learning model 136 as an unsupervised machine-learningmodel. Computing device 104 identifies an element of user hazard data128 utilizing a hazard label 134. In an embodiment, an element of userhazard data 128 may be a hazard label 134. In an embodiment, a hazardlabel 134 may include textual and/or numerical data indicating a user'spredisposition to monetary risk 130. In such an instance, such data maybe identified by computing device 102 as an element of user hazard data128.

With continued reference to FIG. 5 , computing device 102 may calculatea user account profile 120 to contain an element of user personalprofile information 138. User personal profile information includes anyof the personal profile information as described above in more detail inreference to FIG. 1 . Personal profile information may describebehaviors associated with a user such as any concurrent and/or previousbehavior relating to any other fiscal inquiries and/or account operation108. Personal profile information may describe any community behaviors,including a user's ties to the local community and any socialintegration and support systems that the user is engaged upon. One ormore elements of user personal profile information may be stored withinuser database 140. Computing device 102 calculates a user accountprofile 120 to contain at least an element of user personal profileinformation 138.

With continued reference to FIG. 5 , computing device 102 is configuredto calculate a user account profile 120 to contain a user account score142. A user account score 142 includes any of the user account score 142as described above in more detail in reference to FIG. 1 . A useraccount score 142 may include an account history factor, an outstandingaccount factor, an account length factor, and an account type factor.For instance and without limitation, computing device 102 may calculatea user account score 142 that reflects an account history factor thatshows a user has routinely made payments on his credit card on time, anoutstanding account factor that shows a user does not have anyoutstanding debts except for a mortgage on user's house, an accountlength factor that indicates a user has had a bank account with the samebank for the past sixteen years, and an account type factor thatindicates the user currently has a loan on user's house, car, and a homeequity loan.

With continued reference to FIG. 5 , at step 520 computing device 102 isconfigured to generate an account machine-learning model 144. Accountmachine-learning model 144 includes any of the machine-learning modelsas described above in more detail in reference to FIG. 1 . Accountmachine-learning model 144 utilizes a fiscal profile as an input andoutputs an account metric 146. Account metric 146 includes any textual,pictorial, and/or character data that reflects the monetary well-beingand/or monetary stability of a particular user as described above inmore detail in reference to FIG. 1 . Computing device 102 generates anaccount machine-learning model 144 utilizing account training data 148.Account training data 148 includes any of the account training data 148as described above in more detail in reference to FIG. 1 . Accounttraining data 148 includes a plurality of fiscal profiles and aplurality of correlated account metric 146. Computing device 102generates account machine-learning model 144 utilizing account trainingdata 148 and a first machine-learning algorithm. First machine-learningalgorithm includes any of the machine-learning algorithms as describedabove in more detail in reference to FIG. 1 . For instance and withoutlimitation, a first machine-learning algorithm may include a lazylearning algorithm.

With continued reference to FIG. 5 , at step 525 computing device 102 isconfigured to determine a response 150 to an account inquiry 106utilizing an account metric 146. A response 150 includes any of theresponse 150 as described above in more detail in reference to FIG. 1 .Computing device 104 may determine a response 150 to an account inquiry106 based on a user effective age 152. User effective age 152 may becalculated utilizing the calculations as described above in more detailin reference to FIG. 1 . Computing device 102 calculates a usereffective age 152 using a user chronological age and a user biologicalextraction 116. Computing device 102 determines a response 150 to anaccount inquiry 106 utilizing an account metric 146 and a user effectiveage 152. For instance and without limitation, a user effective age 152may reflect that a user who is 47 years old has a user effective age 152of a 25 year old. In such an instance, computing device 102 maydetermine a response 150 to an account inquiry 106 that contains arequest for approval for a twenty five year mortgage and approve themortgage as the user is likely to be alive for the duration of thetwenty five year mortgage. In yet another non-limiting example,computing device 102 may determine a response 150 to an account inquiry106 such as a request for a $50,000 credit card purchase that does notapprove the transaction based on the user's account metric 146 thatindicates the user has excess dopamine and has a history of impulsivemonetary decisions and has a chronological age of 75 and an effectiveage of 84. Computing device 102 is configured to determine that anaccount inquiry 106 is in excess of an account metric 146 and deny anaccount inquiry 106. For instance and without limitation, computingdevice 102 may determine that an account inquiry 106 for a $2800 creditcard purchase is in excess of an account metric 146 that shows a userlives paycheck to paycheck and has $1000 in a savings account. In yetanother non-limiting example, computing device 102 may determine that anaccount inquiry 106 for a $950,000 second mortgage is in excess of anaccount metric 146 that shows a user has $100,000 in a checking accountand saves only 2% of the user's annual income of $40,000. Computingdevice 102 is configured to determine that an account inquiry 106 doesnot exceed an account metric 146 and approve an account inquiry 106. Forinstance and without limitation, computing device 102 may determine thatan account inquiry 106 for a $5000 purchase does not exceed an accountmetric 146 that shows a user has made one time payments on a homemortgage over the past three years and the user has $25,000 in a savingsaccount. In yet another non-limiting example, computing device 102 maydetermine that an account inquiry 106 for a $75,000 car loan does notexceed an account metric 146 that shows a user has a steady income andmade $85,000 the previous year. In such an instance, computing device102 may approve an account inquiry 106.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote device 104 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof.

Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for physiologically informed accountmetrics utilizing artificial intelligence, the system comprising acomputing device, the computing device designed and configured to:receive, from a remote device operated by a third party, an accountinquiry; identify a biological extraction related to the particularuser; calculate a user account profile utilizing the user biologicalextraction, wherein the user account profile contains at least anelement of user hazard data, wherein in hazard data describes a user'spredisposition to monetary risk based on a user's biological extraction;generate an account machine-learning model, wherein the accountmachine-learning model utilizes the user account profile as an input andoutputs an account metric; and determine a response to the accountinquiry utilizing the account metric.
 2. The system of claim 1, whereinthe account inquiry identifies a particular user and an accountoperation related to the particular user.
 3. The system of claim 2,wherein the account operation includes a current proposed monetaryagreement between the particular user and the third-party.
 4. The systemof claim 1, wherein the biological extraction further comprises at leastan element of user physiological data.
 5. The system of claim 1, whereinthe user account profile further contains at least an element of userbehavior data.
 6. The system of claim 1, wherein the computing device isfurther configured to authenticate the account inquiry.
 7. The system ofclaim 1, wherein the computing device is further configured to calculatethe user account profile to contain a user account score.
 8. The systemof claim 7, wherein the user account score further comprises at least anaccount history factor.
 9. The system of claim 1, wherein the computingdevice is further configured to generate the account machine-learningmodel utilizing account training data and a first machine-learningalgorithm.
 10. The system of claim 9, wherein the account training datacomprises a plurality of account profiles and a plurality of correlatedaccount metrics.
 11. A method for physiologically informed accountmetrics utilizing artificial intelligence, the method comprising:receiving, by a computing device, from a remote device operated by athird party, an account inquiry; identifying, by the computing device, abiological extraction related to the particular user; calculating, bythe computing device, a user account profile utilizing the userbiological extraction, wherein the user account profile contains atleast an element of user hazard data, wherein in hazard data describes auser's predisposition to monetary risk based on a user's biologicalextraction; generating, by the computing device, an accountmachine-learning model, wherein the account machine-learning modelutilizes the user account profile as an input and outputs an accountmetric; and determining, by the computing device, a response to theaccount inquiry utilizing the account metric.
 12. The method of claim11, wherein the account inquiry identifies a particular user and anaccount operation related to the particular user.
 13. The method ofclaim 12, wherein the account operation includes a current proposedmonetary agreement between the particular user and the third-party. 14.The method of claim 11, wherein the biological extraction furthercomprises at least an element of user physiological data.
 15. The methodof claim 11, wherein the user account profile further contains at leastan element of user behavior data.
 16. The method of claim 11, furthercomprising authenticating, by the computing device, the account inquiry.17. The method of claim 11, further comprising calculating, by thecomputing device, the user account profile to contain a user accountscore.
 18. The method of claim 17, wherein the user account scorefurther comprises at least an account history factor.
 19. The method ofclaim 1, further comprising generating, by the computing device, theaccount machine-learning model utilizing account training data and afirst machine-learning algorithm.
 20. The method of claim 19, whereinthe account training data comprises a plurality of account profiles anda plurality of correlated account metrics.